Dynamic resource allocation using AI-driven workload forecasting in multi-cloud environments

Abstract

This research investigates the application of artificial intelligence (AI) for dynamic resource allocation using workload forecasting in multi-cloud environments. With the growing adoption of multi-cloud strategies, organizations face increasing challenges in managing resource distribution efficiently due to fluctuating and unpredictable workloads. To address this, the study introduces an AI-driven framework that combines time-series forecasting models such as Long Short-Term Memory (LSTM) networks, reinforcement learning, and decision tree-based algorithms to accurately predict workload demands and allocate resources dynamically across multiple cloud platforms. The system continuously monitors workload patterns and adjusts resource provisioning in real-time to enhance performance and cost-efficiency. Experimental results demonstrate that the proposed approach significantly improves CPU and memory utilization, reduces operational costs by up to 25%, and increases SLA compliance. By offering a scalable, intelligent solution for resource management, this research contributes to the advancement of autonomous cloud operations. It provides practical value for optimizing complex multi-cloud infrastructures' performance, reliability, and efficiency.